Entropy based disease classification of proteomic mass spectrometry data of the human serum by a support vector machine

2014 ◽

pp. 82-100 ◽

Cited By ~ 1

Author(s):

Shiv Ram Dubey ◽

Anand Singh Jalal

Keyword(s):

Support Vector Machine ◽

Apple Scab ◽

Texture Features ◽

Disease Classification ◽

Economic Losses ◽

Support Vector ◽

Test Case ◽

Agricultural Industry ◽

Fruit Disease

Diseases in fruit cause devastating problems in economic losses and production in the agricultural industry worldwide. In this chapter, a method to detect and classify fruit diseases automatically is proposed and experimentally validated. The image processing-based proposed approach is composed of the following main steps: in the first step K-Means clustering technique is used for the defect segmentation, in the second step some color and texture features are extracted from the segmented defected part, and finally diseases are classified into one of the classes by using a multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated the approach for three types of apple diseases, namely apple scab, apple blotch, and apple rot, along with normal apples. The experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The classification accuracy for the proposed approach is achieved up to 93% using textural information and multi-class support vector machine.

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Comparison of Computational Algorithms for the Classification of Liver Cancer using SELDI Mass Spectrometry: A Case Study

Cancer Informatics ◽

10.1177/117693510700300021 ◽

2007 ◽

Vol 3 ◽

pp. 117693510700300 ◽

Cited By ~ 3

Author(s):

Changyu Shen ◽

Timothy E Breen ◽

Lacey E Dobrolecki ◽

C. Max Schmidt ◽

George W. Sledge ◽

...

Keyword(s):

Mass Spectrometry ◽

Hepatocellular Carcinoma ◽

Support Vector Machine ◽

Random Forest ◽

Radial Function ◽

Support Vector ◽

Laser Desorption Ionization ◽

Prediction Analysis

Introduction As an alternative to DNA microarrays, mass spectrometry based analysis of proteomic patterns has shown great potential in cancer diagnosis. The ultimate application of this technique in clinical settings relies on the advancement of the technology itself and the maturity of the computational tools used to analyze the data. A number of computational algorithms constructed on different principles are available for the classification of disease status based on proteomic patterns. Nevertheless, few studies have addressed the difference in the performance of these approaches. In this report, we describe a comparative case study on the classification accuracy of hepatocellular carcinoma based on the serum proteomic pattern generated from a Surface Enhanced Laser Desorption/Ionization (SELDI) mass spectrometer. Methods Nine supervised classification algorithms are implemented in R software and compared for the classification accuracy. Results We found that the support vector machine with radial function is preferable as a tool for classification of hepatocellular carcinoma using features in SELDI mass spectra. Among the rest of the methods, random forest and prediction analysis of microarrays have better performance. A permutation-based technique reveals that the support vector machine with a radial function seems intrinsically superior in learning from the training data since it has a lower prediction error than others when there is essentially no differential signal. On the other hand, the performance of the random forest and prediction analysis of microarrays rely on their capability of capturing the signals with substantial differentiation between groups. Conclusions Our finding is similar to a previous study, where classification methods based on the Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometry are compared for the prediction accuracy of ovarian cancer. The support vector machine, random forest and prediction analysis of microarrays provide better prediction accuracy for hepatocellular carcinoma using SELDI proteomic data than six other approaches.

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Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum

Journal of Computational Biology ◽

10.1089/106652703322756159 ◽

2003 ◽

Vol 10 (6) ◽

pp. 925-946 ◽

Cited By ~ 111

Author(s):

Ryan H. Lilien ◽

Hany Farid ◽

Bruce R. Donald

Keyword(s):

Mass Spectrometry ◽

Human Serum ◽

Disease Classification ◽

Proteomic Data

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Machine Learning for Mass Spectrometry Data Analysis in Proteomics

Current Proteomics ◽

10.2174/1570164617999201023145304 ◽

2020 ◽

Vol 17 ◽

Author(s):

Juntao Li ◽

Kanglei Zhou ◽

Bingyu Mu

Keyword(s):

Machine Learning ◽

Mass Spectrometry ◽

Support Vector Machine ◽

Data Analysis ◽

Rapid Development ◽

Point Of View ◽

Mass Spectrometry Data ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods

: With the rapid development of high-throughput techniques, mass spectrometry has been widely used for largescale protein analysis. To search for the existing proteins, discover biomarkers, and diagnose and prognose diseases, machine learning methods are applied in mass spectrometry data analysis. This paper reviews the applications of five kinds of machine learning methods to mass spectrometry data analysis from an algorithmic point of view, including support vector machine, decision tree, random forest, naive Bayesian classifier and deep learning.

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Bayesian ABC-MCMC Classification of Liquid Chromatography–Mass Spectrometry Data

Cancer Informatics ◽

10.4137/cin.s30798 ◽

2015 ◽

Vol 14s5 ◽

pp. CIN.S30798

Author(s):

Upamanyu Banerjee ◽

Ulisses M. Braga-Neto

Keyword(s):

Mass Spectrometry ◽

Liquid Chromatography ◽

Small Sample ◽

Mass Spectrometry Data ◽

Classification Rule ◽

Support Vector ◽

Liquid Chromatography Mass Spectrometry ◽

Linear Discriminant ◽

Chromatography Mass Spectrometry

Proteomics promises to revolutionize cancer treatment and prevention by facilitating the discovery of molecular biomarkers. Progress has been impeded, however, by the small-sample, high-dimensional nature of proteomic data. We propose the application of a Bayesian approach to address this issue in classification of proteomic profiles generated by liquid chromatography-mass spectrometry (LC-MS). Our approach relies on a previously proposed model of the LC-MS experiment, as well as on the theory of the optimal Bayesian classifier (OBC). Computation of the OBC requires the combination of a likelihood-free methodology called approximate Bayesian computation (ABC) as well as Markov chain Monte Carlo (MCMC) sampling. Numerical experiments using synthetic LC-MS data based on an actual human proteome indicate that the proposed ABC-MCMC classification rule outperforms classical methods such as support vector machines, linear discriminant analysis, and 3-nearest neighbor classification rules in the case when sample size is small or the number of selected proteins used to classify is large.

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